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+EuroLLM-1.7B-Instruct.fq8.gguf filter=lfs diff=lfs merge=lfs -text
+EuroLLM-1.7B-Instruct.silly.gguf filter=lfs diff=lfs merge=lfs -text

EuroLLM-1.7B-Instruct.fq8.gguf

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EuroLLM-1.7B-Instruct.silly.gguf

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README.md

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+
+---
+license: mit
+language:
+- en
+pipeline_tag: text-generation
+---
+
+ZeroWw 'SILLY' version.
+The original model has been quantized (fq8 version)
+and a percentage of it's tensors have
+been modified adding some noise.
+
+Full colab: https://colab.research.google.com/drive/1a7seagBzu5l3k3FL4SFk0YJocl7nsDJw?usp=sharing
+
+Fast colab: https://colab.research.google.com/drive/1SDD7ox21di_82Y9v68AUoy0PhkxwBVvN?usp=sharing
+
+Original reddit post: https://www.reddit.com/r/LocalLLaMA/comments/1ec0s8p/i_made_a_silly_test/
+
+I created a program to randomize the weights of a model. The program has 2 parameters: the percentage of weights to modify and the percentage of the original value to randmly apply to each weight.
+
+At the end I check the resulting GGUF file for binary differences.
+In this example I set to modify 100% of the weights of Mistral 7b Instruct v0.3 by a maximum of 15% deviation.
+
+Since the deviation is calculated on the F32 weights, when quantized to Q8\_0 this changes.
+So, in the end I got a file that compared to the original has:
+
+Bytes Difference percentage: 73.04%
+
+Average value divergence: 2.98%
+
+The cool thing is that chatting with the model I see no apparent difference and the model still works nicely as the original.
+
+Since I am running everything on CPU, I could not run perplexity scores or anything computing intensive.
+
+As a small test, I asked the model a few questions (like the history of the roman empire) and then fact check its answer using a big model. No errors were detected.
+
+Update: all procedure tested and created on COLAB.
+
+Created on: Fri Oct 25, 10:45:32